HLA Class I Supertype Classification Based on Structural Similarity.

HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA-disease association studies.

Enabling Nucleophilic Reactivity in High-Spin Fe(II) Imido Complexes: From Elementary Steps to Cooperative Catalysis.

ConspectusTransition metal complexes featuring an M═NR bond have received great attention as critical intermediates in the synthesis of nitrogen-containing compounds. In general, the properties of the imido ligand in these complexes are dependent on the nature of the metal center. Thus, the imido ligand tends to be nucleophilic in early transition metal complexes and electrophilic in late transition metal complexes. Nonetheless, the supporting ligand can have a dramatic effect on its reactivity. For example, there are sporadic examples of nucleophilic late transition metal imido complexes, often based on strongly donating supporting ligands. Building on these earlier works, in this Article, we show that the imido ligand in a low-coordinate high-spin bis(carbene)borate Fe(II) complex is able to access previously unknown reaction pathways, ultimately leading to new catalytic transformations. We first focus on the synthesis, characterization, and stoichiometric reactivity of a highly nucleophilic Fe(II) imido complex. The entry point for this system is the intermediate-spin three-coordinate Fe(III) imido complex, which is generated from the reaction of an Fe(I) synthon with an organic azide. Alkali metal reduction leads to a series of M+ (M = Li, Na, K) coordinated and charge-separated (M = K(18-C-6)) high-spin Fe(II) imido complexes, all of which have been isolated and fully characterized. Combined with the electronic structure calculations, these results reveal that the alkali ions moderately polarize the Fe═N bond according to K+ ≈ Na+ < Li+. As a result, the basicity of the imido ligand increases from the charged separated complex to K+, Na+, and Li+ coordinated complexes, as validated by intermolecular proton transfer equilibria. The impact of the counterion on imido ligand reactivity is demonstrated through protonation, alkylation, and hydrogen atom abstraction reactions. The counterion also directs the outcome of [2 + 2] reactions with benzophenone, where alkali coordination facilitates double bond metathesis. Building from here, we describe how the unusual nucleophilicity of the high-spin Fe(II) imido complex revealed in stoichiometric reactions can be extended to new catalytic transformations. For example, a [2 + 2] cycloaddition reaction serves as the basis for the catalytic guanylation of carbodiimides under mild conditions. More interestingly, this complex also exhibits the first ene-like reactivity of an M═NR bond in reactions with alkynes, nitriles, and alkenes. These transformations form the basis of catalytic alkyne and nitrile α-deuteration and pKa-dictated alkene transposition reactions, respectively. Mechanistic studies reveal the critical role of metal-ligand cooperativity in facilitating these catalytic transformations and suggest the new avenues for transition metal imido complexes in catalysis that extend beyond classical nitrene transfer chemistry.

Atomistic Simulations of Polydisperse Lignin Melts Using Simple Polydisperse Residue Input Generator.

Understanding the physics of lignin will help rationalize its function in plant cell walls as well as aiding practical applications such as deriving biofuels and bioproducts. Here, we present SPRIG (Simple Polydisperse Residue Input Generator), a program for generating atomic-detail models of random polydisperse lignin copolymer melts i.e., the state most commonly found in nature. Using these models, we use all-atom molecular dynamics (MD) simulations to investigate the conformational and dynamic properties of polydisperse melts representative of switchgrass (Panicum virgatum L.) lignin. Polydispersity, branching and monolignol sequence are found to not affect the calculated glass transition temperature, Tg. The Flory-Huggins scaling parameter for the segmental radius of gyration is 0.42 ± 0.02, indicating that the chains exhibit statistics that lie between a globular chain and an ideal Gaussian chain. Below Tg the atomic mean squared displacements are independent of molecular weight. In contrast, above Tg, they decrease with increasing molecular weight. Therefore, a monodisperse lignin melt is a good approximation to this polydisperse lignin when only static properties are probed, whereas the molecular weight distribution needs to be considered while analyzing lignin dynamics.

Trimethylsilyldiazomethane Disassembly at a Three-Fold Symmetric Iron Site.

The reaction of equimolar trimethylsilyldiazomethyllithium (LiTMSD) with high spin (S = 2) PhB(AdIm)3FeCl (PhB(AdIm)3- = tris(3-adamantylimidazol-2-ylidene)phenylborate) affords the corresponding N-nitrilimido complex PhB(AdIm)3Fe-N═N═C(SiMe3). This complex can be converted to the thermodynamically more favorable C-isocyanoamido isomer PhB(AdIm)3Fe-C═N═N(SiMe3) by reaction with an additional equivalent of LiTMSD. While the iron(II) complexes are four-coordinate, the diazomethane is bound side-on in the iron(I) congener PhB(AdIm)3Fe(N,N'-κ2-N2C(H)Si(CH3)3). The latter complex adopts high spin (S = 3/2) ground state and features an unusually weak C-H bond. Photolysis of the iron(II) complexes induces N═N bond cleavage, with the iron(II) cyanide PhB(AdIm)3Fe-C≡N and iron(IV) nitride PhB(AdIm)3Fe≡N complexes being the major products of the reaction. The same products are obtained when the iron(I) complex is photolyzed or treated with a fluoride source. The trimethylsilyldiazomethane-derived ligand disassembly reactions are contrasted with those observed for related tris(carbene)amine complexes.

Genotype and sleep independently predict mental health in Rett syndrome: an observational study.

BACKGROUND: Rett syndrome is a genetically caused neurodevelopmental disorder associated with severe impairments and complex comorbidities. This study examined predictors of anxiety and depression in Rett syndrome, including genotype. METHODS: The International Rett Syndrome Database, InterRett, was the data source for this observational study. Associations between genotype, functional abilities, comorbidities, anxiety and depression were estimated with univariate and multivariate regression models. An additional regression model for anxiety included use of an anxiety medication as a predictor variable. RESULTS: The sample included 210 individuals aged 6-51 years of whom 54 (25.7%) were on psychotropic medication for anxiety or depression. Individuals with the p.Arg294* variant had the highest anxiety scores, as did those with insomnia or excessive daytime sleepiness, irrespective of anxiety medication use. Individuals with the p.Arg306Cys variant had the lowest depression scores, as did those with insomnia or excessive daytime sleepiness. CONCLUSION: Findings indicated that genotype and sleep have implications for mental health in Rett syndrome, suggesting that anticipatory guidance and proactive management of poor sleep could improve mental health. More research is needed to understand the effects of psychometric medications, which cannot be inferred from this cross-sectional study.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization.

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)-i.e., one of the state-of-the-art multi-building and multi-floor localization models-and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of "by a single building", where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database.

Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

Water molecule ordering on the surface of an intrinsically disordered protein.

The dynamics and local structure of the hydration water on surfaces of folded proteins have been extensively investigated. However, our knowledge of the hydration of intrinsically disordered proteins (IDPs) is more limited. Here, we compare the local structure of water molecules hydrating a globular protein, lysozyme, and the intrinsically disordered N-terminal of c-Src kinase (SH4UD) using molecular dynamics simulation. The radial distributions from the protein surface of the first and the second hydration shells are similar for the folded protein and the IDP. However, water molecules in the first hydration shell of both the folded protein and the IDP are perturbed from the bulk. This perturbation involves a loss of tetrahedrality, which is, however, significantly more marked for the folded protein than the IDP. This difference arises from an increase in the first hydration shell of the IDP of the fraction of hydration water molecules interacting with oxygen. The water ordering is independent of the compactness of the IDP. In contrast, the lifetimes of water molecules in the first hydration shell increase with IDP compactness, indicating a significant impact of IDP configuration on water surface pocket kinetics, which here is linked to differential pocket volumes and polarities.

HLA-Clus: HLA class I clustering based on 3D structure.

BACKGROUND: In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. RESULTS: The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. CONCLUSIONS: The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository ( https://github.com/yshen25/HLA-Clus ).

Catalytic 1,3-Proton Transfer in Alkenes Enabled by Fe═NR Bond Cooperativity: A Strategy for pKa-Dictated Regioselective Transposition of C═C Double Bonds.

Transition metal catalyzed alkene double bond transposition usually involves metal hydride intermediates. Despite significant advances in the design of catalysts that dictate product selectivity, control over substrate selectivity is less advanced and transition metal catalysts that selectively transpose double bonds in substrates containing multiple 1-alkene functionalities are rare. Herein, we report that the three-coordinate high spin (S = 2) Fe(II) imido complex [Ph2B(tBuIm)2Fe═NDipp][K(18-C-6)THF2] (1-K(18-C-6)) catalyzes 1,3-proton transfer from 1-alkene substrates to afford 2-alkene transposition products. Mechanistic investigations involving kinetics, competition, and isotope labeling studies, supported by experimentally calibrated DFT computations, strongly support an unusual nonhydridic mechanism for alkene transposition that is enabled by the cooperative action of the iron center and basic imido ligand. As dictated by the pKa of the allylic protons, this catalyst enables the regioselective transposition of C═C double bonds in substrates containing multiple 1-alkenes. The high spin (S = 2) state of the complex allows a wide scope of functional groups to be tolerated, including those that are typical catalyst poisons, such as amines, N-heterocycles, and phosphines. These results demonstrate a new strategy for metal-catalyzed alkene transposition with predictable substrate regioselectivity.

Reactivity Differences of Trigonal Pyramidal Nonheme Iron(IV)-Oxo and Iron(III)-Oxo Complexes: Experiment and Theory.

High-valent metal-oxo species play critical roles in enzymatic catalysis yet their properties are still poorly understood. In this work we report a combined experimental and computational study into biomimetic iron(IV)-oxo and iron(III)-oxo complexes with tight second-coordination sphere environments that restrict substrate access. The work shows that the second-coordination sphere slows the hydrogen atom abstraction step from toluene dramatically and the kinetics is zeroth order in substrate. However, the iron(II)-hydroxo that is formed has a low reduction potential and hence cannot do OH rebound favorably. The tolyl radical in solution then reacts further with alternative reaction partners. By contrast, the iron(IV)-oxo species reacts predominantly through OH rebound to form alcohol products. Our studies show that the oxidation state of the metal influences reactivities and selectivities with substrate dramatically and that enzymes will likely need an iron(IV) center to catalyze C-H hydroxylation reactions.

Airway-associated adipose tissue accumulation is increased in a kisspeptin receptor knockout mouse model.

Airway-associated adipose tissue increases with body mass index and is a local source of pro-inflammatory adipokines that may contribute to airway pathology in asthma co-existing with obesity. Genetic susceptibility to airway adiposity was considered in the present study through kisspeptin/kisspeptin receptor signalling, known to modulate systemic adiposity and potentially drive airway remodelling. Therefore, the aim of the study was to determine the effects of kisspeptin/kisspeptin receptor signalling in the lung, focusing on airway-associated adipose tissue deposition and impact on airway structure-function. Wild-type, heterozygous and kisspeptin receptor knockout mice were studied at 6 or 8 weeks of age. Lung mechanics were assessed before and after methacholine challenge and were subsequently fixed for airway morphometry. A separate group of mice underwent glucose tolerance testing and bronchoalveolar lavage. At 6 weeks of age, kisspeptin/kisspeptin receptor signalling did not affect body adiposity, airway inflammation, wall structure or function. Despite no differences in body adiposity, there was a greater accumulation of airway-associated adipose tissue in knockout mice. By 8 weeks of age, female knockout mice displayed a non-diabetic phenotype with increased body adiposity but not males. Airway-associated adipose tissue area was also increased in both knockout females and males at 8 weeks of age, but again no other respiratory abnormality was apparent. In summary, airway-associated adipose tissue is decoupled from body adiposity in prepubescent mice which supports a genetic susceptibility to fatty deposits localised to the airway wall. There was no evidence that airway-associated adipose tissue drives pathology or respiratory impairment in the absence of other environmental exposures.

Disordered Domain Shifts the Conformational Ensemble of the Folded Regulatory Domain of the Multidomain Oncoprotein c-Src.

c-Src kinase is a multidomain non-receptor tyrosine kinase that aberrantly phosphorylates several signaling proteins in cancers. Although the structural properties of the regulatory domains (SH3-SH2) and the catalytic kinase domain have been extensively characterized, there is less knowledge about the N-terminal disordered region (SH4UD) and its interactions with the other c-Src domains. Here, we used domain-selective isotopic labeling combined with the small-angle neutron scattering contrast matching technique to study SH4UD interactions with SH3-SH2. Our results show that in the presence of SH4UD, the radius of gyration (Rg) of SH3-SH2 increases, indicating that it has a more extended conformation. Hamiltonian replica exchange molecular dynamics simulations provide a detailed molecular description of the structural changes in SH4UD-SH3-SH2 and show that the regulatory loops of SH3 undergo significant conformational changes in the presence of SH4UD, while SH2 remains largely unchanged. Overall, this study highlights how a disordered region can drive a folded region of a multidomain protein to become flexible, which may be important for allosteric interactions with binding partners. This may help in the design of therapeutic interventions that target the regulatory domains of this important family of kinases.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking.

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

Design of Broadly Cross-Reactive M Protein-Based Group A Streptococcal Vaccines.

Group A streptococcal infections are a significant cause of global morbidity and mortality. A leading vaccine candidate is the surface M protein, a major virulence determinant and protective Ag. One obstacle to the development of M protein-based vaccines is the >200 different M types defined by the N-terminal sequences that contain protective epitopes. Despite sequence variability, M proteins share coiled-coil structural motifs that bind host proteins required for virulence. In this study, we exploit this potential Achilles heel of conserved structure to predict cross-reactive M peptides that could serve as broadly protective vaccine Ags. Combining sequences with structural predictions, six heterologous M peptides in a sequence-related cluster were predicted to elicit cross-reactive Abs with the remaining five nonvaccine M types in the cluster. The six-valent vaccine elicited Abs in rabbits that reacted with all 11 M peptides in the cluster and functional opsonic Abs against vaccine and nonvaccine M types in the cluster. We next immunized mice with four sequence-unrelated M peptides predicted to contain different coiled-coil propensities and tested the antisera for cross-reactivity against 41 heterologous M peptides. Based on these results, we developed an improved algorithm to select cross-reactive peptide pairs using additional parameters of coiled-coil length and propensity. The revised algorithm accurately predicted cross-reactive Ab binding, improving the Matthews correlation coefficient from 0.42 to 0.74. These results form the basis for selecting the minimum number of N-terminal M peptides to include in potentially broadly efficacious multivalent vaccines that could impact the overall global burden of group A streptococcal diseases.

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning.

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure-property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9-76.2 mN/m) and speeds of sound (1009.7-1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure-property relationships in ILs may still be somewhat premature.

American Orthopaedic Foot and Ankle Society Annual Meeting All-in-person Attendance Results in Immense Carbon Expenditure.

BACKGROUND: Professional society conferences are integral to the medical profession. However, airline travel is a major contributor to greenhouse gas production, and the environmental impact of in-person attendance at an orthopaedic conference has yet to be described. With growing concern about the climate crisis, we sought to quantify the carbon footprint of in-person attendance to help potential attendees more consciously consider in-person attendance, inform strategies to minimize greenhouse gas emissions during travel to annual meetings, and increase awareness about and momentum for efforts in orthopaedic surgery to reduce the carbon footprint of society conferences. QUESTIONS/PURPOSES: (1) What was the magnitude of greenhouse gas production resulting from all-in-person 2019 American Orthopaedic Foot and Ankle Society (AOFAS) annual meeting attendance in Chicago, IL, USA? (2) What was the magnitude of greenhouse gas production resulting from the all-virtual 2020 AOFAS annual meeting, and how does it compare with the 2019 AOFAS annual meeting carbon footprint? (3) To what extent could an alternative in-person meeting model with four or seven hubs decrease greenhouse gas production resulting from round-trip air travel compared with the 2019 AOFAS annual meeting? METHODS: A list of the postal codes and countries of all 1271 registered participants attending the four-day 2019 AOFAS annual meeting in Chicago, IL, USA, was obtained from AOFAS headquarters. The 2019 conference was chosen because it was the last pre-COVID meeting and thus attendance was more likely to resemble that at prepandemic in-person conferences than more recent meetings because of pandemic travel restrictions. We estimated carbon dioxide-equivalent (CO 2 e) production from round-trip air travel using a publicly available internet-based calculator (Myclimate: https://co2.myclimate.org/en/flight_calculators/new ). Emissions produced by the conference venue, car travel, and hotel stays were estimated using published Environmental Protection Agency emission factors. To estimate emissions produced by the all-virtual 2020 AOFAS annual meeting (assuming an equal number of attendees as in 2019), we used the framework published by Faber and summed estimated network data transfer emissions, personal computer and monitor emissions, and server-related emissions. Using the 2019 registrant list, we modeled four-hub and seven-hub in-person meeting alternatives to determine potential decreased round-trip air travel greenhouse gas production. Meeting hub locations were selected by visualizing the geographic distribution of the 2019 registrants and selecting reasonable meeting locations that would minimize air travel for the greatest number of attendees. Registrants were assigned to the nearest hub location. Myclimate was again used to estimate CO 2 e production for round-trip air travel for the hub meeting models. RESULTS: The total estimated emissions of the all-in-person 2019 AOFAS annual meeting (when accounting for travel, conference space, and hotel stays) was 1565 tons CO 2 e (median 0.61 tons per attendee, range 0.02 to 7.7 tons). The total estimated emissions of the all-virtual 2020 meeting (when accounting for network data transfer emissions, personal computer and monitor emissions, and server-related emissions) was 34 tons CO 2 e (median 0.03 tons per attendee). This corresponds to a 97.8% decrease in CO 2 e emissions compared with the in-person conference. The model of a four-hub in-person meeting alternative with meetings in Chicago, Santiago, London, and Tokyo predicted an estimated 54% decrease in CO 2 e emissions from round-trip air travel. The seven-hub meeting model with meetings in Chicago; Washington, DC; Dallas; Los Angeles; Santiago; London; and Tokyo was predicted to diminish the CO 2 e emissions of round-trip air travel by an estimated 71%. CONCLUSION: The 2019 AOFAS annual meeting had an enormous carbon footprint and resulted in many individuals exceeding their annual allotted carbon budget (2.5 tons) according to the Paris Agreement. Hosting the meeting virtually greatly reduced the annual meeting carbon footprint, and our hub-based meeting models identified potential in-person alternatives for reducing the carbon footprint of conference attendance. CLINICAL RELEVANCE: Professional societies must consider our responsibility to decarbonizing the healthcare sector by considering innovative approaches-perhaps such as our multihub proposals-to decarbonize carbon-intensive annual meetings without stalling academic progress.

Deconstruction of biomass enabled by local demixing of cosolvents at cellulose and lignin surfaces.

A particularly promising approach to deconstructing and fractionating lignocellulosic biomass to produce green renewable fuels and high-value chemicals pretreats the biomass with organic solvents in aqueous solution. Here, neutron scattering and molecular-dynamics simulations reveal the temperature-dependent morphological changes in poplar wood biomass during tetrahydrofuran (THF):water pretreatment and provide a mechanism by which the solvent components drive efficient biomass breakdown. Whereas lignin dissociates over a wide temperature range (>25 °C) cellulose disruption occurs only above 150 °C. Neutron scattering with contrast variation provides direct evidence for the formation of THF-rich nanoclusters (Rg ∼ 0.5 nm) on the nonpolar cellulose surfaces and on hydrophobic lignin, and equivalent water-rich nanoclusters on polar cellulose surfaces. The disassembly of the amphiphilic biomass is thus enabled through the local demixing of highly functional cosolvents, THF and water, which preferentially solvate specific biomass surfaces so as to match the local solute polarity. A multiscale description of the efficiency of THF:water pretreatment is provided: matching polarity at the atomic scale prevents lignin aggregation and disrupts cellulose, leading to improvements in deconstruction at the macroscopic scale.

Scope of the Problem: Ankle Arthroscopy.

Ankle arthroscopy has seen increased utilization and application in recent years. Through the advent of improved instrumentation and techniques, indications have been expanded to include the management of traumatic, degenerative, inflammatory, and neoplastic conditions. It is important to review anterior and posterior ankle arthroscopies along with the history, pertinent anatomy, techniques, indications, and complications as well as gain insight into the future of ankle arthroscopy.

Scope of the Problem: Tendoscopy and Small Joint Arthroscopy of the Foot and Ankle.

It is important to identify and describe practical applications of arthroscopy in the management of foot and ankle pathology. Utilization of the arthroscope provides a minimally invasive means of evaluating and addressing pathology. It obviates the need for a large open approach, which has additional value in the setting of a multiprocedure surgery. In addition to reducing surgical time, arthroscopy provides a potentially enhanced field of view and an adequate working space to address injury. As interest in minimally invasive options grows, the need for safe, effective tendoscopic and arthroscopic options in the foot and ankle increases. A clear and high-yield reference is needed with which to approach these procedures.